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The association between social media use and sleep disturbance among young adults
Open accessAbstract
Introduction Many factors contribute to sleep disturbance among young adults. Social media (SM) use is increasing rapidly, and little is known regarding its association with sleep disturbance. Methods In 2014 we assessed a nationally-representative sample of 1788 U.S. young adults ages 19-32. SM volume and frequency were assessed by self-reported minutes per day spent on SM (volume) and visits per week (frequency) using items adapted from the Pew Internet Research Questionnaire. We assessed sleep disturbance using the brief Patient-Reported Outcomes Measurement Information System (PROMIS®) Sleep Disturbance measure. Analyses performed in Pittsburgh utilized chi-square tests and ordered logistic regression using sample weights in order to estimate effects for the total U.S. population. Results In models that adjusted for all sociodemographic covariates, participants with higher SM use volume and frequency had significantly greater odds of having sleep disturbance. For example, compared with those in the lowest quartile of SM use per day, those in the highest quartile had an AOR of 1.95 (95% CI = 1.37-2.79) for sleep disturbance. Similarly, compared with those in the lowest quartile of SM use frequency per week, those in the highest quartile had an AOR of 2.92 (95% CI = 1.97-4.32) for sleep disturbance. Associations all demonstrated a significant linear trend. Discussion The strong association between SM use and sleep disturbance has important clinical implications for the health and well-being of young adults. Future work should aim to assess directionality and to better understand the influence of contextual factors associated with SM use.
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TL;DR
In a nationally representative sample of U.S. young adults ages 19–32, greater self-reported social media use was associated with higher odds of sleep disturbance. The association was stronger for frequency of social media visits than for total daily volume.
Key findings
- Median social media use was 61 minutes per day (IQR = 30, 135) and 30 visits per week (IQR = 8.5, 56.5).
- Weighted sleep disturbance distribution was 42.6% low, 28.0% medium, and 29.4% high.
- In fully adjusted models, compared with the lowest quartile of daily social media volume, the highest quartile had an AOR of 1.95 (95% CI = 1.37–2.79) for higher sleep disturbance.
- Compared with the lowest quartile of weekly social media frequency, the highest quartile had an AOR of 2.92 (95% CI = 1.97–4.32) for higher sleep disturbance; associations showed a significant linear trend (all P < .001).
Methods
- Cross-sectional analysis of 1,788 U.S. young adults ages 19–32 from the GfK KnowledgePanel, using data collected in the 18-month follow-up survey from October to November 2014.
- Social media use was self-reported as average daily minutes (volume) and weekly visits across 11 platforms (frequency); sleep disturbance was assessed with four PROMIS Sleep Disturbance short-form items and grouped into low, medium, and high tertiles.
- Chi-square tests and ordered logistic regression with survey weights were used, adjusting for sociodemographic covariates; sensitivity analyses used more parsimonious models, no survey weights, and continuous social media variables.
Limitations
- The paper states that the cross-sectional design prevents determining directionality or causality between social media use and sleep disturbance.
- The paper states reliance on retrospective self-report of social media use and sleep, without formal sleep studies or ecological momentary assessment, and notes that results cannot be generalized beyond ages 19–32.
- The paper states that the 4-item PROMIS short form, rather than computerized adaptive testing, may have contributed to non-normal response data and limited sleep disturbance to an ordinal-categorical variable; it also notes that future work should examine a broader set of covariates such as mental health status.
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12 Not checked yet · 11 Own result · 8 Own method · 5 Own interpretation · 1 Supported · 1 Sources not found
Every source the paper cites for this claim was checked and supports it.
In the paper
“While nearly all research in this area has focused on children and adolescents, 96% of young adults in the 18-30 year-old range use some type of technology—such as cell phones (67%), computers (60%), and electronic music devices (43%)—before bed.4”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > INTRODUCTION
What the sources say
- Supportedcites [4] Gradisar M Wolfson AR Harvey AG Hale L Rosenberg R Czeisler CA The sleep and technology use of Americans: findings from the National Sleep Foundation’s 2011 Sleep in America poll J Clin Sleep Med 2013 9 1291 1299 24340291 PMC3836340 source
The Results (Technology Presence and Use) report that 96% of adults younger than 30 used some form of technology in the bedroom in the hour before sleep, and give the cited device percentages for young adults. The Methods define young adults as ages 19–29, so the citing sentence’s “18–30 year-old range” is approximate. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Associations all demonstrated a significant linear trend.”
- Also stated in: § Multivariable Analyses · boosted
“Post-estimate tests demonstrated linearity of the overall association between each of the independent variables and the dependent variable in both unadjusted and adjusted models (all P < .001).”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Results
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Multivariable Analyses · boosted
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Median volume was 61 minutes of SM use per day (IQR = 30, 135; range = 0-1447), while median frequency was 30 visits per week (IQR=8.5, 56.5; range=0-385).”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Social Media Use
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, compared with those in the lowest quartile of SM use per day, those in the highest quartile had an AOR of 1.95 (95% CI = 1.37-2.79) for sleep disturbance.”
- Also stated in: § Multivariable Analyses · plain
“For example, compared with those in the lowest quartile, those in the highest quartile had an AOR of 1.95 (95% CI = 1.37 - 2.79) for having a higher level of sleep disturbance.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
- Also stated in: § Multivariable Analyses · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Median volume was 61 minutes of SM use per day (IQR = 30, 135; range = 0-1447), while median frequency was 30 visits per week (IQR=8.5, 56.5; range=0-385).”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Social Media Use
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In models that adjusted for all sociodemographic covariates, participants with higher SM use volume and frequency had significantly greater odds of having sleep disturbance.”
- Also stated in: § Multivariable Analyses · plain
“In fully-adjusted models (Table 2), participants with higher SM volume had significantly greater odds of having sleep disturbance.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
- Also stated in: § Multivariable Analyses · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among the demographic variables, sex and level of household income were significantly associated with sleep disturbance. Females were more likely to have high levels of sleep disturbance than males, and lower levels of income were associated with higher levels of sleep disturbance.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Bivariable Analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among the demographic variables, sex and level of household income were significantly associated with sleep disturbance. Females were more likely to have high levels of sleep disturbance than males, and lower levels of income were associated with higher levels of sleep disturbance.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Bivariable Analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, compared with those in the lowest quartile of SM use frequency per week, those in the highest quartile had an AOR of 2.92 (95% CI = 1.97-4.32) for sleep disturbance.”
- Also stated in: § Multivariable Analyses · plain
“Similarly, compared with those in the lowest quartile for frequency of use, those in the highest quartile had an AOR of 2.92 (95% CI = 1.97 - 4.32) for having a higher level of sleep disturbance.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
- Also stated in: § Multivariable Analyses · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This may indicate that the frequency of visits is a better predictor of sleep difficulty than overall SM time.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 8
- Probably not a substantial claim. 13
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is interesting that associations with sleep disturbance were higher for the frequency variable than for the volume variable. For example, compared to those in the lowest quartile for frequency, those in the highest quartile had an AOR of about 3 for increased sleep disturbance, while for the volume variable the AOR was about 2.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SM volume and frequency were significantly associated with sleep disturbance, with greater level of SM use associated with higher levels of disturbance.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Bivariable Analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accounting for survey weights, 42.6% of our sample was in the low sleep disturbance group, 28.0% was in the medium disturbance group, and 29.4% was in the high sleep disturbance group.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Sleep
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The PROMIS Sleep Disturbance assessment, in particular, reflects a global measure of sleep, rather than focusing on a particular disorder.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Measures
What the sources say
- Not checked yetcites [31] Buysse DJ Yu L Moul DE Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments Sleep 2010 33 781 792 20550019 PMC2880437 source
- Not checked yetcites [32] Yu L Buysse DJ Germain A Development of short forms from the PROMIS Sleep Disturbance and Sleep-Related Impairment item banks Behav Sleep Med 2012 10 6 24 22250775 PMC3261577 source
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Previous work has described the development and psychometric testing of the PROMIS sleep disturbance measure among individuals age 18 and older. 31;32”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Measures
What the sources say
- Not checked yetcites [31] Buysse DJ Yu L Moul DE Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments Sleep 2010 33 781 792 20550019 PMC2880437 source
- Not checked yetcites [32] Yu L Buysse DJ Germain A Development of short forms from the PROMIS Sleep Disturbance and Sleep-Related Impairment item banks Behav Sleep Med 2012 10 6 24 22250775 PMC3261577 source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The decision to include all covariates in the models was made a priori based on their possible impact on sleep disturbance.35”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Analysis
What the sources say
- Not checked yetcites [35] Grandner MA Patel NP Gehrman PR Who gets the best sleep? Ethnic and socioeconomic factors related to sleep complaints Sleep Med 2010 11 470 478 20388566 PMC2861987
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“PROMIS measures have the advantage of being highly reliable and precise measures of patient-reported health, utilizing short forms of its measures to lessen participant burden while maintaining precision across a range of domains.33;34”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Measures
What the sources say
- Not matchedcites [33] NIH About PROMIS. PROMIS website. 2015 12 22 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [34] NIH Selecting an Instrument. PROMIS website. 2015 12 22 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, missing data accounted for between 0.4-1.6%, depending on the analysis.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Participants
plainn = 1,788- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Two recent reviews in this area demonstrated an inverse association between electronic media use and sleep parameters such as longer time to fall asleep, delayed bedtime, and reduced total sleep time.20;21”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > INTRODUCTION
What the sources say
- Not checked yetcites [20] Cain N Gradisar M Electronic media use and sleep in school-aged children and adolescents: A review Sleep Med 2010 11 735 742 20673649 source
- Not checked yetcites [21] Hale L Guan S Screen time and sleep among school-aged children and adolescents: a systematic literature review Sleep Med Rev 2015 21 50 58 25193149 PMC4437561 source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Alternatively, those who have difficulty sleeping may subsequently spend more time using SM.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
What the sources say
- Not checked yetcites [37] Tavernier R Willoughby T Sleep problems: predictor or outcome of media use among emerging adults at university? J Sleep Res 2014 23 389 396 24552437 source
- Not checked yetcites [38] Eggermont S Van den Bulck J Nodding off or switching off? The use of popular media as a sleep aid in secondary-school children J Paediatr Child Health 2006 42 428 433 16898880 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We conducted all primary analyses using survey weights, which were calculated by GfK and applied using a post-stratification adjustment based on socio-demographic benchmark distributions.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Analysis
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We first conducted appropriate diagnostics to ensure that the data satisfied the proportional-odds assumption, which is necessary to conduct ordered logistic regression.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the findings presented here are cross-sectional, it is not possible to determine whether SM use contributes to sleep disturbance, sleep disturbance contributes to SM use, or both.”
- Also stated in: § Strengths and Limitations · plain
“As noted above, one important limitation of the cross-sectional nature of the data is that we cannot determine the direction of association between SM use and sleep disturbance, nor can we attribute causality to any effect.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Strengths and Limitations · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Responses were received from 1796 participants (55.2%), of which 1,788 participants (54.9%) had complete sleep data and were included in the analysis.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > METHODS > Procedures
plainn = 1,788- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“It was a necessary limitation that we relied on participants’ retrospective report of their SM use and sleep in completing the survey. For example, in this large population it was not feasible to intensively validate sleep patterns using formal sleep studies or to validate SM use with ecological momentary assessment.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION > Strengths and Limitations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It may also be that both of these hypotheses are true—difficulty sleeping may lead to increased use of SM, which may in turn lead to more problems sleeping.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“However, another study among 11-13 year olds in the Midlands region of the United Kingdom demonstrated that, compared with SM, use of other technologies such as television, music, and video games was more substantially associated with sleep problems.22”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > INTRODUCTION
What the sources say
- Not checked yetcites [22] Arora T Broglia E Thomas EN Taheri S Associations between specific technologies and adolescent sleep quantity, sleep quality, and parasomnias Sleep Med 2014 15 240 247 24394730 source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The strong association between SM use and sleep disturbance has important clinical implications for the health and well-being of young adults.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > Abstract > Discussion
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
- Rests on an assumption: The 11 social media platforms assessed capture the relevant social media use for this population.
- Rests on an assumption: The sample remains nationally representative after excluding participants with missing data.
The paper relies on this without support.
Self-reported estimates of time spent on social media accurately reflect participants' actual social media use.
“It was a necessary limitation that we relied on participants’ retrospective report of their SM use and sleep in completing the survey.”
The paper relies on this without support.
The 11 social media platforms assessed capture the relevant social media use for this population.
“The frequency variable was adopted from a Pew Internet Research survey asking participants to indicate how frequently they visit or use each of the 11 most popular SM platforms: Facebook, YouTube, Twitter, Google Plus, Instagram, Snapchat, Reddit, Tumblr, Pinterest, Vine, and LinkedIn.”
The paper relies on this without support.
The sample remains nationally representative after excluding participants with missing data.
“Responses were received from 1796 participants (55.2%), of which 1,788 participants (54.9%) had complete sleep data and were included in the analysis.”
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In the citing paper
“Previous studies have found a significant association between social media use and measures of sleep disturbance (including long sleep onset latency and difficulty falling asleep) when controlling for: age and sex19; sociodemographic measures21 and sleep hygiene behaviours.15”
From Social media use and adolescent sleep patterns: cross-sectional findings from the UK millennium cohort study (2019) · cites this paper as [21]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“This indicates generally heavier social media use compared with young adults21 and provides a current normative profile for UK adolescents.”
From Social media use and adolescent sleep patterns: cross-sectional findings from the UK millennium cohort study (2019) · cites this paper as [21]
The cited source says what the claim attributes to it.
In the citing paper
“Numerous studies have reported a strong association between social network sites use and poorer sleep quality among young adults [20–22].”
From A serial mediation model of social media addiction and college students' academic engagement: the role of sleep quality and fatigue (2023) · cites this paper as [20]
In the citing paper
“These findings have supported links between higher overall social media use and poorer sleep outcomes, including shorter sleep duration, later bedtimes and poorer sleep quality [17,26].”
From Understanding Links Between Social Media Use, Sleep and Mental Health: Recent Progress and Current Challenges (2019) · cites this paper as [26]
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Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Participants
“There were complete data for the independent and dependent variables for 1788 participants.”
§ The Association between Social Media Use and Sleep Disturbance among Young Adults > RESULTS > Participants
“As is noted in Table 1, 1768 participants reported SM volume (minutes per day), while 1781 reported SM frequency (visits per week).”
Citations
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Users of the world, unite! The challenges and opportunities of Social Media
Kaplan, A. M., & Haenlein, M. (2010) · Business Horizons · doi:10.1016/j.bushor.2009.09.003 · Cited by 17,875 · cited 2×
“(Kaplan and Haenlein, 2010) The rapidly growing rate of SM use in recent years (Duggan et al., 2015) raises concern that SM usemay adversely affect sleep quality and may displace total amount of sleep (Zimmerman, 2008).”
BackgroundNational Sleep Foundation's sleep time duration recommendations: methodology and results summary.
Hirshkowitz, M., Whiton, K., Albert, S. M., Alessi, C., Bruni, O., DonCarlos, L., Hazen, N., Herman, J., Katz, E. S., Kheirandish-Gozal, L., et al. (2015) · Sleep Health · doi:10.1016/j.sleh.2014.12.010 · Cited by 4,404 · cited 1×
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BackgroundSleep Health: Can We Define It? Does It Matter?
Buysse, D. J. (2014) · SLEEP · doi:10.5665/sleep.3298 · Cited by 2,768 · cited 1×
“Sleep and circadian functioning are essential to promoting good health (Buysse, 2014).”
BackgroundBehavioral and Physiological Consequences of Sleep Restriction
Banks, S., & Dinges, D. F. (2007) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.26918 · Cited by 1,505 · cited 1×
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BackgroundScreen time and sleep among school-aged children and adolescents: a systematic literature review
Hale, L., & Guan, S. (2015) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2014.07.007 · Cited by 1,468 · cited 1×
“This is because many forms of SM involve interactive screen time, whichmay bemore stimulating and engaging, and thus potentially detrimental to sleep – compared with more passive activities such as watching television and reading books (Hale and Guan, 2015).”
BackgroundChang, A.-M., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418490112 · Cited by 1,430 · cited 1×
“Third, bright light emitted by SM devices may delay circadian rhythms (Chang et al., 2015).”
BackgroundInsufficient Sleep in Adolescents and Young Adults: An Update on Causes and Consequences
Owens, J., ADOLESCENT SLEEP WORKING GROUP, COMMITTEE ON ADOLESCENCE, Au, R., Carskadon, M., Millman, R., Wolfson, A., Braverman, P. K., Adelman, W. P., Breuner, C. C., et al. (2014) · Pediatrics · doi:10.1542/peds.2014-1696 · Cited by 1,413 · cited 1×
“This includes biological changes in the accumulation of homeostatic sleep pressure (the likelihood of falling asleep), increasing academic and vocational demands, and use of substances such as alcohol and caffeine (Moore and Meltzer, 2008; Hershner and Chervin, 2014; Owens, 2014; Millman, 2005).”
BackgroundElectronic media use and sleep in school-aged children and adolescents: A review
Cain, N., & Gradisar, M. (2010) · Sleep Medicine · doi:10.1016/j.sleep.2010.02.006 · Cited by 1,376
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“We assessed sleep disturbance using four items from the Patient-Reported Outcomes Measurement Information System (PROMIS®) Sleep Disturbance measure (Buysse et al., 2010; Yu et al., 2012), which assessed problems with sleep, difficulty falling asleep, whether sleep was refreshing, and sleep quality over the past 7 day…”
BackgroundMethodBuysse, D. J., Yu, L., Moul, D. E., Germain, A., Stover, A., Dodds, N. E., Johnston, K. L., Shablesky-Cade, M. A., & Pilkonis, P. A. (2010) · Sleep · doi:10.1093/sleep/33.6.781 · Cited by 850 · cited 3×
“We assessed sleep disturbance using four items from the Patient-Reported Outcomes Measurement Information System (PROMIS®) Sleep Disturbance measure (Buysse et al., 2010; Yu et al., 2012), which assessed problems with sleep, difficulty falling asleep, whether sleep was refreshing, and sleep quality over the past 7 day…”
BackgroundMethod
Cited by
Sorted by citations
Children and Adolescents and Digital Media
Reid Chassiakos, Y. (., Radesky, J., Christakis, D., Moreno, M. A., Cross, C., COUNCIL ON COMMUNICATIONS AND MEDIA, Hill, D., Ameenuddin, N., Hutchinson, J., Levine, A., et al. (2016) · Pediatrics · doi:10.1542/peds.2016-2593 · Cited by 1,340
Twenge, J. M., Martin, G. N., & Spitzberg, B. H. (2019) · Psychology of Popular Media Culture · doi:10.1037/ppm0000203 · Cited by 703
Media Use in School-Aged Children and Adolescents
COUNCIL ON COMMUNICATIONS AND MEDIA, Hill, D., Ameenuddin, N., Chassiakos, Y. (. R., Cross, C., Radesky, J., Hutchinson, J., Levine, A., Boyd, R., Mendelson, R., et al. (2016) · Pediatrics · doi:10.1542/peds.2016-2592 · Cited by 622
Social media? It's serious! Understanding the dark side of social media
Baccarella, C. V., Wagner, T. F., Kietzmann, J., & McCarthy, I. P. (2018) · European Management Journal · doi:10.1016/j.emj.2018.07.002 · Cited by 504
Wong, H. Y., Mo, H. Y., Potenza, M. N., Chan, M. N. M., Lau, W. M., Chui, T. K., Pakpour, A. H., & Lin, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17061879 · Cited by 546
“The relationship between SMA severity and sleep quality may reflect cognitive and emotional arousal when using social media before sleep, with increased arousal preventing sleep [60].”
BackgroundRecent increases in depressive symptoms among US adolescents: trends from 1991 to 2018
Keyes, K. M., Gary, D., O’Malley, P. M., Hamilton, A., & Schulenberg, J. (2019) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-019-01697-8 · Cited by 465
Interplay between social media use, sleep quality, and mental health in youth: a systematic review
Alonzo, R., Hussain, J., Stranges, S., & Anderson, K. K. (2021) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2020.101414 · Cited by 456
Kolhar, M., Kazi, R. N. A., & Alameen, A. A. M. (2021) · Saudi Journal of Biological Sciences · doi:10.1016/j.sjbs.2021.01.010 · Cited by 456
“This can lead to a sedentary lifestyle and increase one’s risk of developing non-communicable diseases such as diabetes, obesity, and hypertension (Melkevik et al., 2015; Zou et al., 2019; Hu et al., 2001).”
BackgroundTwitter and Research: A Systematic Literature Review Through Text Mining
Karami, A., Lundy, M., Webb, F., & Dwivedi, Y. K. (2020) · IEEE Access · doi:10.1109/access.2020.2983656 · Cited by 286
“…Twitter-related issues such as investigating social media users’ preference for oral health information searching [196], analyzing the relationship between gender, personality, and Twitter addiction [197], sleep disturbance and social media use [198], and eating concerns and social media use [199].”
BackgroundYou, Y., Chen, Y., Wen, F., Li, X., Wang, R., Liu, J., & Ma, X. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1080782 · Cited by 263
The Negative Effects of Digital Technology Usage on Children’s Development and Health
Mustafaoğlu, R., Zirek, E., Yasacı, Z., & Özdi̇nçler, A. R. (2018) · Addicta The Turkish Journal on Addictions · doi:10.15805/addicta.2018.5.2.0051 · Cited by 246
Is boredom proneness related to social media overload and fatigue? A stress–strain–outcome approach
Whelan, E., Islam, A. K. M. N., & Brooks, S. L. (2020) · Internet Research · doi:10.1108/intr-03-2019-0112 · Cited by 252
“Fatigue can also be caused by poor sleep quality and emerging research links the extent of social media use to sleep curtailment (Levenson et al., 2016; Woods and Scott, 2016).”
BackgroundSedentary Behavior and Sleep Problems: a Systematic Review and Meta-Analysis
Yan, Y., Shin, J. C., Li, D., & An, R. (2016) · International Journal of Behavioral Medicine · doi:10.1007/s12529-016-9609-0 · Cited by 255
Digital media use and sleep in late adolescence and young adulthood: A systematic review
Brautsch, L. A., Lund, L., Andersen, M. M., Jennum, P. J., Folker, A. P., & Andersen, S. (2023) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2022.101742 · Cited by 218
Tandon, A., Kaur, P., Dhir, A., & Mäntymäki, M. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106487 · Cited by 209
Scott, H., Biello, S. M., & Woods, H. C. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-031161 · Cited by 221
“15 17 21 In contrast, first establishing what constitutes typical use and then comparing outcomes for relatively higher or lower users against this reference point can support more meaningful conclusions.”
BackgroundResultThe Internet and Children’s Psychological Wellbeing
McDool, E., Powell, P., Roberts, J., & Taylor, K. (2020) · Journal of Health Economics · doi:10.1016/j.jhealeco.2019.102274 · Cited by 189
Dresp, B., & Hutt, A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19116910 · Cited by 230
Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A. (2017) · Sleep · doi:10.1093/sleep/zsx113 · Cited by 177
Self-citationShimoga, S. V., Erlyana, E., & Rebello, V. (2019) · Journal of Medical Internet Research · doi:10.2196/14290 · Cited by 153
“While a high risk of poor-quality sleep or sleep disturbance is associated with frequent social media use among adolescents [11,12,14-16], there is some indication that sleeplessness precedes excessive media use [17,18].”
Effect of diurnal fasting on sleep during Ramadan: a systematic review and meta-analysis
Faris, M. E., Jahrami, H., Alhayki, F. A., Alkhawaja, N. A., Ali, A. M., Aljeeb, S. H., Abdulghani, I. H., & BaHammam, A. S. (2019) · Sleep And Breathing · doi:10.1007/s11325-019-01986-1 · Cited by 159
Social media addiction and burnout: The mediating roles of envy and social media use anxiety
Liu, C., & Ma, J.-L. (2018) · Current Psychology · doi:10.1007/s12144-018-9998-0
Evers, K., Chen, S., Rothmann, S., Dhir, A., & Pallesen, S. (2020) · Journal of Adolescence · doi:10.1016/j.adolescence.2020.08.011 · Cited by 135
The dark side of social media: Stalking, online self‐disclosure and problematic sleep
Dhir, A., Talwar, S., Kaur, P., Budhiraja, S., & Islam, A. K. M. N. (2021) · International Journal of Consumer Studies · doi:10.1111/ijcs.12659 · Cited by 161
Family Digital Literacy Practices and Children’s Mobile Phone Use
Terras, M. M., & Ramsay, J. E. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01957 · Cited by 164
“Evidence indicates that the four parenting styles identified by Baumrind influence both the nature and number of internet mediation strategies used.”
Backgroundvan der Schuur, W. A., Baumgartner, S. E., & Sumter, S. R. (2018) · Health Communication · doi:10.1080/10410236.2017.1422101 · Cited by 145
“Whereas one study showed that SM frequency was related to more sleep disturbances, including more difficulty falling asleep and lower sleep quality (Levenson et al., 2016), other studies showed limited or no support for a negative relationship between SM use and sleep (Garett et al.”
BackgroundResultInfluentialKwok, C., Leung, P. Y., Poon, K.-C., & Fung, X. C. C. (2021) · Asian Journal of Social Health and Behavior · doi:10.4103/shb.shb_81_20 · Cited by 150
Loneliness in the lives of Danish adolescents: Associations with health and sleep
Eccles, A. M., Qualter, P., Madsen, K. R., & Holstein, B. E. (2020) · Scandinavian Journal of Public Health · doi:10.1177/1403494819865429 · Cited by 125
“Another limitation in relation to the measurement of sleep in the current study is the lack of other confounding variables – including social media and use of technology before bedtime – that are important in current discussion relating to sleep [51,52].”
BackgroundMammalian circadian systems: Organization and modern life challenges
Finger, A., & Kramer, A. (2021) · Acta Physiologica · doi:10.1111/apha.13548 · Cited by 117
Bhat, S., Pinto-Zipp, G., Upadhyay, H., & Polos, P. G. (2018) · Sleep Health · doi:10.1016/j.sleh.2017.12.004 · Cited by 114
“”(5) Similarly, there appears to be a clear association between increased ESM use and anxiety,(6) as well as depression, and frequency of texting and duration of ESM use both during the day and at bedtimehave been shown to be predictive of sleep disturbances.(8,10,11) Given its addictive nature, it is not surprising t…”
BackgroundChinoy, E. D., Duffy, J. F., & Czeisler, C. A. (2018) · Physiological Reports · doi:10.14814/phy2.13692 · Cited by 116
“…the evenings, as is typical among adolescents and young adults who frequently multitask using different LE-devices (Van den Bulck 2007; Foley et al. 2013; Gradisar et al. 2013; Arora et al. 2014; Gamble et al. 2014; Hale and Guan 2015; Hysing et al. 2015; Lemola et al. 2015; Levenson et al. 2016).”
BackgroundInfluentialAkdeniz, G., Kavakcı, M., Gozugok, M., Yalcinkaya, S., Küçükay, A., & Şahutoğulları, B. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00695 · Cited by 110
The relationship between social media use and sleep quality among undergraduate students
Garett, R., Liu, S., & Young, S. D. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1266374 · Cited by 111
Fanning, J. T., Porter, G. C., Awick, E. A., Ehlers, D. K., Roberts, S. A., Cooke, G. E., Burzynska, A. Z., Voss, M. W., Kramer, A. F., & McAuley, E. (2016) · Journal of Behavioral Medicine · doi:10.1007/s10865-016-9788-9 · Cited by 120
“For instance, in very recent years, a number of publications have drawn associations between greater social networking website usage, as well as higher levels of digital media usage before bedtime, with poorer sleep outcomes in young adults (Levenson et al., 2016; Orzech et al., 2016).”
Hughes, N., & Burke, J. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.03.047 · Cited by 101
Scott, H., & Woods, H. C. (2019) · Current Sleep Medicine Reports · doi:10.1007/s40675-019-00148-9 · Cited by 105
Malaeb, D. N., Salameh, P., Barbar, S., Awad, E., Haddad, C., Hallit, R. R., Sacre, H., Akel, M., Obeïd, S., & Hallit, S. R. (2020) · Perspectives In Psychiatric Care · doi:10.1111/ppc.12576 · Cited by 105
“84 On the other hand, some studies provided different hypotheses about the effect of the SM use on sleeping patterns.”
Marker, C., Gnambs, T., & Appel, M. (2017) · Educational Psychology Review · doi:10.1007/s10648-017-9430-6 · Cited by 120
Samuelsson, L. B., Bovbjerg, D. H., Roecklein, K. A., & Hall, M. H. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.10.011 · Cited by 94
“The average human in industrialized societies today is regularly exposed to a significant degree of LAN and chronodisruption, regardless of shift work status, particularly in relation to the recent surge in technological availability and nighttime use (Vijakkhana et al., 2015; Levenson et al., 2016) and overall countr…”
BackgroundThe dark side of mobile money: Perspectives from an emerging economy
Mogaji, E., & Nguyen, N. P. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122045 · Cited by 93
The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review
Yu, D. J., Wing, Y. K., Li, T. M. H., & Chan, N. Y. (2024) · Current Psychiatry Reports · doi:10.1007/s11920-024-01481-9 · Cited by 140
Salari, N., Zarei, H., Hosseinian-Far, A., Rasoulpoor, S., Shohaimi, S., & Mohammadi, M. (2025) · Journal of Public Health · doi:10.1007/s10389-023-02012-1 · Cited by 91
“Sleep disorders are also associated with an increased risk of metabolic disorders such as obesity, hypertension, and diabetes (Hershner and Chervin 2014; Knutson and Cau-ter 2008; Levenson et al. 2016).”
Identifying drivers for bedtime social media use despite sleep costs: The adolescent perspective
Scott, H., Biello, S. M., & Woods, H. C. (2019) · Sleep Health · doi:10.1016/j.sleh.2019.07.006 · Cited by 95
Hjetland, G. J., Skogen, J. C., Hysing, M., & Sivertsen, B. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.794307 · Cited by 107
“These findings suggest a significant role of screen use in relation to students’ sleep quantity and quality, and are in line with previous studies on students and young adults (14, 15, 24).”
BackgroundResultAlbqoor, M. A., & Shaheen, A. M. (2020) · Sleep And Breathing · doi:10.1007/s11325-020-02188-w · Cited by 86
Sampasa‐Kanyinga, H., Hamilton, H. A., & Chaput, J. (2018) · Acta Paediatrica · doi:10.1111/apa.14210 · Cited by 96
“(20) found that heavy use of social media was significantly associated with greater odds of sleep disturbances.”
BackgroundZhuang, J., Mou, Q., Zheng, T., Gao, F., Zhong, Y., Lu, Q., Gao, Y., & Zhao, M. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-04799-5 · Cited by 104
Primack, B. A., & Escobar-Viera, C. G. (2017) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2016.12.007 · Cited by 71
“Researchers examined a nationally representative sample of young adults and found that increased SM use was independently associated with poor sleep.(74) Sleep disturbance was assessed using the brief PROMIS sleep disturbance measure.”
MethodResultSelf-citationDas‐Friebel, A., Lenneis, A., Realo, A., Sanborn, A., Tang, N. K. Y., Wolke, D., von Mühlenen, A., & Lemola, S. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13326 · Cited by 71
“On the other hand, it has been linked to a range of negative consequences, for example to poorer sleep (Levenson et al., 2016, 2017), lower affective wellbeing (Sagioglou & Greitemeyer, 2014; Weinstein, 2018), and poorer mental health (Keles et al., 2019).”
BackgroundDigital Technology and Media Use by Adolescents: Latent Class Analysis
Moreno, M. A., Binger, K., Zhao, Q., Eickhoff, J., Minich, M., & Uhls, Y. T. (2022) · JMIR Pediatrics and Parenting · doi:10.2196/35540 · Cited by 66
“Previous studies have illustrated that digital technology and media use is associated with negative outcomes such as impaired sleep [4-6], decreased physical activity [5,7,8], problematic internet use (PIU) [9-11], and risk of depression [12].”
BackgroundSocial media use of adolescents who died by suicide: Lessons from a psychological autopsy study
Balt, E., Mérelle, S., Robinson, J., Popma, A., Creemers, D., van den Brand, I., van Bergen, D., Rasing, S., Mulder, W., & Gilissen, R. (2023) · Child and Adolescent Psychiatry and Mental Health · doi:10.1186/s13034-023-00597-9 · Cited by 66
“International evidence reports associations between the frequency and volume of social media use and sleep disturbance [12] as well as loneliness and depression [13, 14], and highlights the risks of upward social comparison [15–17].”
BackgroundPrevalence of smartphone addiction and its correlates in a sample of Egyptian university students
Okasha, T. A., Saad, A. E., Ibrahim, I., ElHabiby, M. M., Khalil, S. A., & Morsy, M. H. A. (2021) · International Journal of Social Psychiatry · doi:10.1177/00207640211042917 · Cited by 68
“of 64% 57%, for sleep disturbance respectively among American university students (Becker et al., 2018; Levenson et al., 2016) and in Arab countries studies showed similar rates as in Egypt 53% (Elwasify et al.”
ResultInfluentialAzhari, A., Toms, Z., Pavlopoulou, G., Esposito, G., & Dimitriou, D. (2022) · Acta Psychologica · doi:10.1016/j.actpsy.2022.103706 · Cited by 92
“Beyond normative social media activities, two studies on 12-to 18-year-old adolescents and youths by Vernon et al. (2015) and Levenson et al. (2016) respectively demonstrated that excessive social media usage, in particular, aggravated the quality of sleep.”
“Leave your smartphone out of bed”: quantitative analysis of smartphone use effect on sleep quality
Kheirinejad, S., Visuri, A., Ferreira, D., & Hosio, S. J. (2022) · Personal and Ubiquitous Computing · doi:10.1007/s00779-022-01694-w · Cited by 80
Current trends in digital media: How and why teens use technology
Chassiakos, Y. R., & Stager, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817319-0.00002-5 · Cited by 79
Digital media use and subsequent self-harm during a 1-year follow-up of Chinese adolescents
Wang, L., Liu, X., Liu, Z., & Jia, C.-X. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.05.066 · Cited by 54
Kaur, P., Dhir, A., Alkhalifa, A. K., & Tandon, A. (2021) · Internet Research · doi:10.1108/intr-04-2020-0187 · Cited by 55
Savcı, M., Akat, M., Ercengiz, M., Griffiths, M. D., & Aysan, F. (2020) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-020-00410-0 · Cited by 60
“It has been found that PSMU is positively associated with depression, anxiety, psychological stress, sleep disorders, and cyber victimization (Byrne et al. 2018; Keles et al. 2019; Levenson et al. 2016).”
BackgroundMa, Y., & Qin, X. (2021) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2021.100982 · Cited by 61
Facebook use and sleep quality: Light interacts with socially induced alertness
Bowler, J., & Bourke, P. A. (2018) · British Journal of Psychology · doi:10.1111/bjop.12351 · Cited by 60
Rashid, S. M. M., Mawah, J., Banik, E., Akter, Y., Deen, J. I., Jahan, A., Khan, N. M., Rahman, M. M., Lipi, N., Akter, F., et al. (2021) · Health Science Reports · doi:10.1002/hsr2.388 · Cited by 85
Effects of a sleep hygiene text message intervention on sleep in college students
Gipson, C. S., Chilton, J. M., Dickerson, S. S., Alfred, D., & Haas, B. K. (2019) · Journal of American College Health · doi:10.1080/07448481.2018.1462816 · Cited by 50
Swain, R. K., & Pati, A. K. (2019) · Biological Rhythm Research · doi:10.1080/09291016.2019.1620487 · Cited by 53
“The light emitted from these devices reinforces the message to the brain to stay active and awake (Levenson et al. 2016).”
BackgroundThe influence of parents and peers on adolescents’ problematic social media use revealed
Leijse, M. M. L., Koning, I. M., & van den Eijnden, R. J. J. M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107705 · Cited by 69
Combertaldi, S. L., Ort, A., Cordi, M. J., Fahr, A., & Rasch, B. (2021) · Sleep Medicine · doi:10.1016/j.sleep.2021.09.009 · Cited by 48
Boyle, S. C., Baez, S., Trager, B. M., & LaBrie, J. W. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19169847 · Cited by 49
“Yet, research has also highlighted potential dark sides of SM, with studies linking greater time on SM to a myriad of negative outcomes, including depression [4,5]), body image concerns [6,7]), sleep disturbance [8,9], alcohol use [10,11], misperceived health behavior norms [12,13], and cyberbullying victimization [14…”
BackgroundHua, Z., Wang, S., & Yuan, X. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.009 · Cited by 46
Myhr, A., Anthun, K. S., Lillefjell, M., & Sund, E. R. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01472 · Cited by 43
“Although the internet, social media, and online gaming offer numerous positive opportunities for connection, an increasing number of incidents have involved excessive/pathological use that may cause stress and lead to behavioral addiction, increased nondirectional social comparisons and loneliness, low self-esteem, lo…”
BackgroundHamilton, J. L., Chand, S., Reinhardt, L., Ladouceur, C. D., Silk, J. S., Moreno, M. A., Franzen, P. L., & Bylsma, L. M. (2020) · Journal of Adolescence · doi:10.1016/j.adolescence.2020.07.009 · Cited by 50
Sedentary Behavior and Lack of Physical Activity among Children in Indonesia
Hanifah, L., Nasrulloh, N., & Sufyan, D. L. (2023) · Children · doi:10.3390/children10081283 · Cited by 124
“A study published in the Journal of Adolescence found that excessive use of electronic devices, especially social media, was associated with poorer sleep quality and increased symptoms of depression and anxiety in adolescents [44].”
BackgroundMoreno, M. A., Binger, K. S., Zhao, Q., & Eickhoff, J. C. (2019) · Journal of Medical Internet Research · doi:10.2196/16736 · Cited by 39
“However, digital technology use has also been associated with negative health outcomes including impaired sleep [3-5], decreased physical activity [4,6,7], problematic internet use [8-10], and risk for depression [11,12].”
BackgroundBae, M. (2022) · Internet Research · doi:10.1108/intr-05-2021-0269 · Cited by 52
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“When sleep quality deteriorates, individuals’ coping abilities may weaken, making them more prone to falling into a cycle of emotion-focused coping strategies and mobile phone dependency (Levenson et al., 2016).”
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“The share of teenagers using Facebook has decreased rapidly in the United States, with YouTube and other online social media being more commonly used [5].”
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Impact of social media on Youth: Comprehensive Analysis
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“Apart from sleep duration, a nationally representative survey of young US adults aged 19–32 years showed that about half experienced some self-reported problems with sleep, difficulty falling asleep, non-refreshing sleep, or poor perceived sleep quality [2], although it is not clear how many met the threshold for chro…”
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“[23] Similarly, a study among US young adult ages 19-32 nationally representative sample demonstrated a strong association between social media use and sleep disturbance.[24] Likewise, another study conducted on Spanish adolescents aged 17-18 years suggests a positive relationship between telecommunication and other s…”
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“…for children’s’ inactive life-styles and the deficit of physical activity (Clements 2004; Herrington and Brussoni 2015), an increase in physical musculoskeletal problems (Harris and Straker 2000; Kelly, Dockrell, and Galvin 2009) and sleep disturbances (Cespedes et al. 2014; Levenson et al. 2016).”
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A Stimulated Recall Method for the Improved Assessment of Quantity and Quality of Social Media Use
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“Yet there is strikingly little consensus on the matter, as illustrated by two recent literature reviews [11,12]: some studies indicate a negative relationship between social media use and well-being [6,13] and others a positive relationship [5,14,15].”
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Metadata
- Authors
- Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Primack, Brian A.
- Venue
- Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 85
- Pages
- 36-41
- ISSN
- 0091-7435 (ISSN-L)
- PMC
- PMC4857587
- OpenAlex
- W2235566653
- Semantic Scholar
- 5e2c889b53c3b0bda312ee55d2aa24306f465416
- Cited by
- 447
- References
- 33
- Citations by year
- 2026: 53 · 2025: 59 · 2024: 60 · 2023: 55 · 2022: 47 · 2021: 48 · 2020: 50 · 2019: 32 · 2018: 19 · 2017: 12 · 2016: 11
- FWCI
- 35.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01-CA140150, T32-HL082610)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Keywords
- social media use, sleep disorders, young adults, social media use frequency, sleep health
- MeSH terms
- Patient Reported Outcome Measures, Adult, Female, Humans, Longitudinal Studies, Male, Surveys and Questionnaires, Sleep Wake Disorders, Time Factors, United States, Chi-Square Distribution, Logistic Models, Young Adult, Social Media
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